Whole-genome sequence of Pseudomonas benzopyrenica MLY92: isolation from diseased leaves of tobacco in China.

Here we present a sketch of the whole-genome sequence of Pseudomonas benzopyrenica. The strain comes from the leaf veins of a diseased tobacco plant. This study has significant research implications for gaining insights into the characteristics of microorganisms belonging to the genus Pseudomonas.

Turning bad into good: A medium-entropy double perovskite oxide with beneficial surface reconstruction for active and robust cathode of solid oxide fuel cells.

The cathodes of solid oxide fuel cells (SOFCs) often suffer from detrimental cation segregations and associated impurities poisoning, leading to insufficient electroactivity and poor stability. Here we developed a medium-entropy double perovskite GdBa(Co1.2Mn0.2Fe0.2Ni0.2Cu0.2)O5-δ (ME-GBCO) for promising SOFC cathode. The increased configuration entropy can effectively tailor the surface composition with in situ formed active BaCoO3-δ (BCO) species, rather than inert and deleterious BaOx segregation on parent GdBaCo2O5-δ (GBCO) surface. Accordingly, the layered ME-GBCO cathode with beneficial surface reconstruction exhibited not only high oxygen reduction activity but excellent durability against CO2 impurity, enabling it a very attractive cathode for intermediate temperature SOFCs (IT-SOFCs). Our study provides a new idea for development of efficient and durable cathodes via configurational entropy induced rational surface reconstruction.

Degradation Mechanisms of Electrodes Promotes Direct Regeneration of Spent Li-Ion Batteries: A Review.

The rapid growth of electric vehicle use is expected to cause a significant environmental problem in the next few years due to the large number of spent lithium-ion batteries (LIBs). Recycling spent LIBs will not only alleviate the environmental problems but also address the challenge of limited natural resources shortages. While several hydro- and pyrometallurgical processes are developed for recycling different components of spent batteries, direct regeneration presents clear environmental, and economic advantages. The principle of the direct regeneration approach is restoring the electrochemical performance by healing the defective structure of the spent materials. Thus, the development of direct regeneration technology largely depends on the formation mechanism of defects in spent LIBs. This review systematically details the degradation mechanisms and types of defects found in diverse cathode materials, graphite anodes, and current collectors during the battery's lifecycle. Building on this understanding, principles and methodologies for directly rejuvenating materials within spent LIBs are outlined. Also the main challenges and solutions for the large-scale direct regeneration of spent LIBs are proposed. Furthermore, this review aims to pave the way for the direct regeneration of materials in discarded lithium-ion batteries by offering a theoretical foundation and practical guidance.

Adeno-to-squamous transition drives resistance to KRAS inhibition in LKB1 mutant lung cancer.

KRASG12C inhibitors (adagrasib and sotorasib) have shown clinical promise in targeting KRASG12C-mutated lung cancers; however, most patients eventually develop resistance. In lung patients with adenocarcinoma with KRASG12C and STK11/LKB1 co-mutations, we find an enrichment of the squamous cell carcinoma gene signature in pre-treatment biopsies correlates with a poor response to adagrasib. Studies of Lkb1-deficient KRASG12C and KrasG12D lung cancer mouse models and organoids treated with KRAS inhibitors reveal tumors invoke a lineage plasticity program, adeno-to-squamous transition (AST), that enables resistance to KRAS inhibition. Transcriptomic and epigenomic analyses reveal ΔNp63 drives AST and modulates response to KRAS inhibition. We identify an intermediate high-plastic cell state marked by expression of an AST plasticity signature and Krt6a. Notably, expression of the AST plasticity signature and KRT6A at baseline correlates with poor adagrasib responses. These data indicate the role of AST in KRAS inhibitor resistance and provide predictive biomarkers for KRAS-targeted therapies in lung cancer.

Molecular Basis for SPINDOC-Spindlin1 Engagement and Its Role in Transcriptional Attenuation.

Spindlin1 is a histone reader with three Tudor-like domains and its transcriptional co-activator activity could be attenuated by SPINDOC. The first two Tudors are involved in histone methylation readout, while the function of Tudor 3 is largely unknown. Here our structural and binding studies revealed an engagement mode of SPINDOC-Spindlin1, in which a hydrophobic motif of SPINDOC, DOCpep3, stably interacts with Spindlin1 Tudor 3, and two neighboring K/R-rich motifs, DOCpep1 and DOCpep2, bind to the acidic surface of Spindlin1 Tudor 2. Although DOCpep3-Spindlin1 engagement is compatible with histone readout, an extended SPINDOC fragment containing the K/R-rich region attenuates histone or TCF4 binding by Spindlin1 due to introduced competition. This inhibitory effect is more pronounced for weaker binding targets but not for strong ones such as H3 "K4me3-K9me3" bivalent mark. Further ChIP-seq and RT-qPCR indicated that SPINDOC could promote genomic relocation of Spindlin1, thus modulate downstream gene transcription. Collectively, we revealed multivalent engagement between SPINDOC and Spindlin1, in which a hydrophobic motif acts as the primary binding site for stable SPINDOC-Spindlin1 association, while K/R-rich region modulates the target selectivity of Spindlin1 via competitive inhibition, therefore attenuating the transcriptional co-activator activity of Spindlin1.

Oxygen-enriched pitch-derived hierarchically porous carbon toward boosted zinc-ion storage performance.

The lack of cathode materials with satisfactory Zn2+ storage capability substantially hinders the realization of high-performance aqueous zinc-ion hybrid capacitors (ZHCs). Herein, we propose a facile KMnO4 template-assisted KOH activation strategy to prepare a novel oxygen-enriched hierarchically porous carbon (HPC-1-4). This strategy efficiently converts coal tar pitch (CTP) into a well-tuned carbon material with a large specific surface area of 3019 m2 g-1 and a high oxygen content of 9.20 at%, which is conducive to providing rich active sites, rapid charge transport, and appreciable pseudocapacitance for Zn-ion storage. Thus, the as-fabricated HPC-1-4-based aqueous ZHC exhibits prominent performance, including a high gravimetric capacity (206.7 mAh g-1 at 0.25 A g-1), a remarkable energy density (153.4 Wh kg-1 at 184.2 W kg-1), and an impressive power output (15240 W kg-1 at 63.5 Wh kg-1). In-depth ex-situ characterizations indicate that the excellent electrochemical properties of ZHCs are due to the synergistic effect of the Zn2+ adsorption mechanism and reversible chemisorption. In addition, the assembled quasi-solid-state device demonstrates excellent electrochemical stability of up to 100% capacity retention over 50000 cycles, accompanied with a desirable energy density of 115.6 Wh kg-1. The facile preparation method of converting CTP into carbonaceous functional materials has advanced the development of efficient and eco-friendly energy storage technologies.

iSMOD: an integrative browser for image-based single-cell multi-omics data.

Genomic and transcriptomic image data, represented by DNA and RNA fluorescence in situ hybridization (FISH), respectively, together with proteomic data, particularly that related to nuclear proteins, can help elucidate gene regulation in relation to the spatial positions of chromatins, messenger RNAs, and key proteins. However, methods for image-based multi-omics data collection and analysis are lacking. To this end, we aimed to develop the first integrative browser called iSMOD (image-based Single-cell Multi-omics Database) to collect and browse comprehensive FISH and nucleus proteomics data based on the title, abstract, and related experimental figures, which integrates multi-omics studies focusing on the key players in the cell nucleus from 20 000+ (still growing) published papers. We have also provided several exemplar demonstrations to show iSMOD's wide applications-profiling multi-omics research to reveal the molecular target for diseases; exploring the working mechanism behind biological phenomena using multi-omics interactions, and integrating the 3D multi-omics data in a virtual cell nucleus. iSMOD is a cornerstone for delineating a global view of relevant research to enable the integration of scattered data and thus provides new insights regarding the missing components of molecular pathway mechanisms and facilitates improved and efficient scientific research.

Genome architecture and selective signals compensatorily shape plastic response to a new environment.

Transcriptional plasticity interacts with natural selection in complex ways and is crucial for the survival of species under rapid climate change. How 3D genome architecture affects transcriptional plasticity and its interaction with genetic adaptation are unclear. We transplanted estuarine oysters to a new environment and found that genes located in active chromatin regions exhibited greater transcriptional plasticity, and changes in these regions were negatively correlated with selective signals. This indicates a trade-off between 3D active regions and selective signals in shaping plastic responses to a new environment. Specifically, a mutation, lincRNA, and changes in the accessibility of a distal enhancer potentially affect its interaction with the ManⅡa gene, which regulates the muscle function and survival of oysters. Our findings reveal that 3D genome architecture compensates for the role of genetic adaptation in environmental response to new environments and provide insights into synergetic genetic and epigenetic interactions critical for fitness-related trait and survival in a model marine species.

(Tn5-)FISH-based imaging in the era of 3D/spatial genomics.

3D genomics mainly focuses on the 3D position of single genes at the cell level, while spatial genomics focuses more on the tissue level. In this exciting new era of 3D/spatial genomics, half-century old FISH and its derivative methods, including Tn5-FISH, play important roles. In this review, we introduce the Tn5-FISH we developed recently, and present six different applications published by our collaborators and us, based on (Tn5-)FISH, which can be either general BAC clone-based FISH or Tn5-FISH. In these interesting cases, (Tn5-)FISH demonstrated its vigorous ability of targeting sub-chromosomal structures across different diseases and cell lines (leukemia, mESCs (mouse embryonic stem cells), and differentiation cell lines). Serving as an effective tool to image genomic structures at the kilobase level, Tn5-FISH holds great potential to detect chromosomal structures in a high-throughput manner, thus bringing the dawn for new discoveries in the great era of 3D/spatial genomics.

A review on multimodal machine learning in medical diagnostics.

Nowadays, the increasing number of medical diagnostic data and clinical data provide more complementary references for doctors to make diagnosis to patients. For example, with medical data, such as electrocardiography (ECG), machine learning algorithms can be used to identify and diagnose heart disease to reduce the workload of doctors. However, ECG data is always exposed to various kinds of noise and interference in reality, and medical diagnostics only based on one-dimensional ECG data is not trustable enough. By extracting new features from other types of medical data, we can implement enhanced recognition methods, called multimodal learning. Multimodal learning helps models to process data from a range of different sources, eliminate the requirement for training each single learning modality, and improve the robustness of models with the diversity of data. Growing number of articles in recent years have been devoted to investigating how to extract data from different sources and build accurate multimodal machine learning models, or deep learning models for medical diagnostics. This paper reviews and summarizes several recent papers that dealing with multimodal machine learning in disease detection, and identify topics for future research.

Efficient lattice-based revocable attribute-based encryption against decryption key exposure for cloud file sharing.

Cloud file sharing (CFS) has become one of the important tools for enterprises to reduce technology operating costs and improve their competitiveness. Due to the untrustworthy cloud service provider, access control and security issues for sensitive data have been key problems to be addressed. Current solutions to these issues are largely related to the traditional public key cryptography, access control encryption or attribute-based encryption based on the bilinear mapping. The rapid technological advances in quantum algorithms and quantum computers make us consider the transition from the tradtional cryptographic primitives to the post-quantum counterparts. In response to these problems, we propose a lattice-based Ciphertext-Policy Attribute-Based Encryption(CP-ABE) scheme, which is designed based on the ring learing with error problem, so it is more efficient than that designed based on the learing with error problem. In our scheme, the indirect revocation and binary tree-based data structure are introduced to achieve efficient user revocation and dynamic management of user groups. At the same time, in order to further improve the efficiency of the scheme and realize file sharing across enterprises, the scheme also allows multiple authorities to jointly set up system parameters and manage distribute keys. Furthermore, by re-randomizing the user's private key and update key, we achieve decryption key exposure resistance(DKER) in the scheme. We provide a formal security model and a series of security experiments, which show that our scheme is secure under chosen-plaintext attacks. Experimental simulations and evaluation analyses demonstrate the high efficiency and practicality of our scheme.

Development and Experimental Validation of a Novel Prognostic Signature for Gastric Cancer.

BACKGROUND: Gastric cancer is a malignant tumor with high morbidity and mortality. Therefore, the accurate recognition of prognostic molecular markers is the key to improving treatment efficacy and prognosis. METHODS: In this study, we developed a stable and robust signature through a series of processes using machine-learning approaches. This PRGS was further experimentally validated in clinical samples and a gastric cancer cell line. RESULTS: The PRGS is an independent risk factor for overall survival that performs reliably and has a robust utility. Notably, PRGS proteins promote cancer cell proliferation by regulating the cell cycle. Besides, the high-risk group displayed a lower tumor purity, higher immune cell infiltration, and lower oncogenic mutation than the low-PRGS group. CONCLUSIONS: This PRGS could be a powerful and robust tool to improve clinical outcomes for individual gastric cancer patients.

Single-cell and spatial transcriptome analyses revealed cell heterogeneity and immune environment alternations in metastatic axillary lymph nodes in breast cancer.

BACKGROUND: Tumor heterogeneity plays essential roles in developing cancer therapies, including therapies for breast cancer (BC). In addition, it is also very important to understand the relationships between tumor microenvironments and the systematic immune environment. METHODS: Here, we performed single-cell, VDJ sequencing and spatial transcriptome analyses on tumor and adjacent normal tissue as well as axillar lymph nodes (LNs) and peripheral blood mononuclear cells (PBMCs) from 8 BC patients. RESULTS: We found that myeloid cells exhibited environment-dependent plasticity, where a group of macrophages with both M1 and M2 signatures possessed high tumor specificity spatially and was associated with worse patient survival. Cytotoxic T cells in tumor sites evolved in a separate path from those in the circulatory system. T cell receptor (TCR) repertoires in metastatic LNs showed significant higher consistency with TCRs in tumor than those in nonmetastatic LNs and PBMCs, suggesting the existence of common neo-antigens across metastatic LNs and primary tumor cites. In addition, the immune environment in metastatic LNs had transformed into a tumor-like status, where pro-inflammatory macrophages and exhausted T cells were upregulated, accompanied by a decrease in B cells and neutrophils. Finally, cell interactions showed that cancer-associated fibroblasts (CAFs) contributed most to shaping the immune-suppressive microenvironment, while CD8+ cells were the most signal-responsive cells. CONCLUSIONS: This study revealed the cell structures of both micro- and macroenvironments, revealed how different cells diverged in related contexts as well as their prognostic capacities, and displayed a landscape of cell interactions with spatial information.

SOTIP is a versatile method for microenvironment modeling with spatial omics data.

The rapidly developing spatial omics generated datasets with diverse scales and modalities. However, most existing methods focus on modeling dynamics of single cells while ignore microenvironments (MEs). Here we present SOTIP (Spatial Omics mulTIPle-task analysis), a versatile method incorporating MEs and their interrelationships into a unified graph. Based on this graph, spatial heterogeneity quantification, spatial domain identification, differential microenvironment analysis, and other downstream tasks can be performed. We validate each module's accuracy, robustness, scalability and interpretability on various spatial omics datasets. In two independent mouse cerebral cortex spatial transcriptomics datasets, we reveal a gradient spatial heterogeneity pattern strongly correlated with the cortical depth. In human triple-negative breast cancer spatial proteomics datasets, we identify molecular polarizations and MEs associated with different patient survivals. Overall, by modeling biologically explainable MEs, SOTIP outperforms state-of-art methods and provides some perspectives for spatial omics data exploration and interpretation.

1-Tetradecanol, Diethyl Phthalate and Tween 80 Assist in the Formation of Thermo-Responsive Azoxystrobin Nanoparticles.

The occurrence of crop fungal diseases is closely related to warm environmental conditions. In order to control the release of fungicides in response to warm conditions, and enhance the efficacy, a series of thermo-responsive fungicide-loaded nanoparticles were developed. The fungicide azoxystrobin, solvent DEP, emulsifier Tween 80 and thermo-responsive component TDA were combined to create thermal-response oil phases, conditions for emulsification were then optimized. LDLS, zeta potential, FTIR, DSC, TGA, XRD, SEM and antifungal efficacy assays were carried out to investigate the characteristics and forming mechanism. The results indicated that the formula with 5 g azoxystrobin, 10 mL DEP, 6 mL Tween 80 and 2.5 g TDA constructed the proposed oil phase with the ability to transform from solid at 20 °C to softerned at 31.5 °C. Both DEP and TDA played key roles in interfering with the crystallization of azoxystrobin. The optimal T3t-c12 nanoparticles had a mean particle size of 162.1 nm, thermo-responsive morphological transformation between 20 °C and 30 °C, AZO crystal reforming after drying, the ability to attach to fungal spores and satisfied antifungal efficacy against P. nicotiana PNgz07 and A. niger A1513 at 30 °C. This report provides referable technical support for the construction of smart-release nanoparticles of other agrochemicals.

The transcriptional coactivator RUVBL2 regulates Pol II clustering with diverse transcription factors.

RNA polymerase II (Pol II) apparatuses are compartmentalized into transcriptional clusters. Whether protein factors control these clusters remains unknown. In this study, we find that the ATPase-associated with diverse cellular activities (AAA + ) ATPase RUVBL2 co-occupies promoters with Pol II and various transcription factors. RUVBL2 interacts with unphosphorylated Pol II in chromatin to promote RPB1 carboxy-terminal domain (CTD) clustering and transcription initiation. Rapid depletion of RUVBL2 leads to a decrease in the number of Pol II clusters and inhibits nascent RNA synthesis, and tethering RUVBL2 to an active promoter enhances Pol II clustering at the promoter. We also identify target genes that are directly linked to the RUVBL2-Pol II axis. Many of these genes are hallmarks of cancers and encode proteins with diverse cellular functions. Our results demonstrate an emerging activity for RUVBL2 in regulating Pol II cluster formation in the nucleus.

CTCF organizes inter-A compartment interactions through RYBP-dependent phase separation.

Chromatin is spatially organized into three-dimensional structures at different levels including A/B compartments, topologically associating domains and loops. The canonical CTCF-mediated loop extrusion model can explain the formation of loops. However, the organization mechanisms underlying long-range chromatin interactions such as interactions between A-A compartments are still poorly understood. Here we show that different from the canonical loop extrusion model, RYBP-mediated phase separation of CTCF organizes inter-A compartment interactions. Based on this model, we designed and verified an induced CTCF phase separation system in embryonic stem cells (ESCs), which facilitated inter-A compartment interactions, improved self-renewal of ESCs and inhibited their differentiation toward neural progenitor cells. These findings support a novel and non-canonical role of CTCF in organizing long-range chromatin interactions via phase separation.

[Establishment of a CFTR-based detection method for the second messenger cAMP in the cytoplasm].

Objective: To establish a detection method based on Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) that can sensitively detect the second messenger cyclic AMP (cAMP) in the cytoplasm. Methods: The eukaryotic expression vectors of CFTR and YFP-H148Q / I152L were constructed respectively. FRT cells co-expressing CFTR and YFP-H148Q / I152L were obtained by liposome transfection. The expression of CFTR and YFP-H148Q / I152L in FRT cells was observed by an inverted fluorescence microscopy, and flow cytometry was used to detect the purity of cells; The cell model was identified by the fluorescence quenching kinetics test. The validation of the cell model which could screen CFTR modulators was verified by the fluorescence quenching kinetics experiments. The radioimmunoassay was used to detect the cAMP concentration in cytoplasm after adding CFTR activator. Results: The results of the inverted fluorescence microscope showed that CFTR was expressed in the cell membrane and YFP-H148Q / I152L was expressed in the cytoplasm of FRT cells. The FRT cell model stably co-expressing ANO1 and YFP-H148Q / I152L was successfully constructed. The model could screen CFTR modulators, and the slope of fluorescence change and the concentration of CFTR modulators were in a dose-dependent manner. The slope of the fluorescence could reflect the cAMP concentration in the cytoplasm. The cell model could sensitively detect the intracellular cAMP concentration. Conclusion: The cell model could efficiently and sensitively detect the second messenger cAMP concentration in the cytoplasm, and it provided a simple and efficient method for the study of other targets associated cAMP signal.

Polarization modulation with optical lock-in detection reveals universal fluorescence anisotropy of subcellular structures in live cells.

The orientation of fluorophores can reveal crucial information about the structure and dynamics of their associated subcellular organelles. Despite significant progress in super-resolution, fluorescence polarization microscopy remains limited to unique samples with relatively strong polarization modulation and not applicable to the weak polarization signals in samples due to the excessive background noise. Here we apply optical lock-in detection to amplify the weak polarization modulation with super-resolution. This novel technique, termed optical lock-in detection super-resolution dipole orientation mapping (OLID-SDOM), could achieve a maximum of 100 frames per second and rapid extraction of 2D orientation, and distinguish distance up to 50 nm, making it suitable for monitoring structural dynamics concerning orientation changes in vivo. OLID-SDOM was employed to explore the universal anisotropy of a large variety of GFP-tagged subcellular organelles, including mitochondria, lysosome, Golgi, endosome, etc. We found that OUF (Orientation Uniformity Factor) of OLID-SDOM can be specific for different subcellular organelles, indicating that the anisotropy was related to the function of the organelles, and OUF can potentially be an indicator to distinguish normal and abnormal cells (even cancer cells). Furthermore, dual-color super-resolution OLID-SDOM imaging of lysosomes and actins demonstrates its potential in studying dynamic molecular interactions. The subtle anisotropy changes of expanding and shrinking dendritic spines in live neurons were observed with real-time OLID-SDOM. Revealing previously unobservable fluorescence anisotropy in various samples and indicating their underlying dynamic molecular structural changes, OLID-SDOM expands the toolkit for live cell research.

ScaffComb: A Phenotype-Based Framework for Drug Combination Virtual Screening in Large-Scale Chemical Datasets.

Combinational therapy is used for a long time in cancer treatment to overcome drug resistance related to monotherapy. Increased pharmacological data and the rapid development of deep learning methods have enabled the construction of models to predict and screen drug pairs. However, the size of drug libraries is restricted to hundreds to thousands of compounds. The ScaffComb framework, which aims to bridge the gaps in the virtual screening of drug combinations in large-scale databases, is proposed here. Inspired by phenotype-based drug design, ScaffComb integrates phenotypic information into molecular scaffolds, which can be used to screen the drug library and identify potent drug combinations. First, ScaffComb is validated using the US food and drug administration dataset and known drug combinations are successfully reidentified. Then, ScaffComb is applied to screen the ZINC and ChEMBL databases, which yield novel drug combinations and reveal an ability to discover new synergistic mechanisms. To our knowledge, ScaffComb is the first method to use phenotype-based virtual screening of drug combinations in large-scale chemical datasets.